Qwen1.5-MoE-A2.7B

A large-scale MoE (Mixture of Experts) language model whose performance rivals that of 70 billion parameter models.

EditorRecommendationProgrammingNatural Language ProcessingLarge Model

Qwen1.5-MoE-A2.7B is a large-scale MoE (Mixture of Experts) language model with only 2.7 billion activation parameters. Despite its smaller size, it achieves performance comparable to 70 billion parameter models. Compared to traditional large models, this model reduces training costs by 75% and increases inference speed by 1.74 times. It employs a special MoE architecture design, including fine-grained experts, new initialization methods, and routing mechanisms, which significantly enhance model efficiency. This model is suitable for various tasks in natural language processing, code generation, and more.

Natural Language Processing
Code Generation
Multilingual Support
Low Training Cost
High Inference Efficiency

It can be used in applications such as dialogue systems
intelligent writing assistants
question answering systems
and code autocompletion.

Develop an automatic writing assistant tool based on this model
providing high-quality text generation capabilities.
Integrate this model into a code editor to implement intelligent code completion and optimization features.
Build a multilingual question answering system using this model to provide users with high-quality answers.

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Qwen1.5-MoE-A2.7B

Qwen1.5-MoE-A2.7B Visit Over Time

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Qwen2.5-Coder-1.5B-Instruct-GPTQ-Int4 — A code generation model with 1.5 billion parameters from the Qwen2.5-Coder series.

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